This model is intended for research, education, and computational biology assistance. It is not a medical device and should not be used for clinical diagnosis, treatment decisions, or professional medical advice.

Model Details

Base model: google/gemma-4-12B-it
Fine-tuning method: QLoRA / SFT, merged into full model
Domain: bioinformatics, genomics, transcriptomics, proteomics, sequence analysis, biological databases, and common bioinformatics tools
Primary format: Hugging Face transformers
GGUF format: available in yashm/gemma4-12b-bioinfo-GGUF

Quick Start: Transformers

Gemma 4 uses AutoModelForImageTextToText in this notebook, not AutoModelForCausalLM.

python

import torch
from transformers import AutoTokenizer, AutoModelForImageTextToText

repo_id = "yashm/gemma4-12b-bioinfo"

tokenizer = AutoTokenizer.from_pretrained(
    repo_id,
    trust_remote_code=True,
)

model = AutoModelForImageTextToText.from_pretrained(
    repo_id,
    device_map="auto",
    dtype=torch.bfloat16,
    attn_implementation="eager",
    trust_remote_code=True,
)

system_prompt = (
    "You are an expert bioinformatics assistant with deep knowledge of "
    "genomics, proteomics, transcriptomics, sequence analysis, biological "
    "databases, and bioinformatics tools. Provide accurate, concise, and "
    "scientifically rigorous answers."
)

messages = [
    {"role": "system", "content": system_prompt},
    {"role": "user", "content": "Explain the difference between local and global sequence alignment."},
]

prompt = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True,
)

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)

with torch.inference_mode():
    output_ids = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=True,
        temperature=0.2,
        top_p=0.9,
        repetition_penalty=1.1,
        pad_token_id=tokenizer.pad_token_id,
        eos_token_id=tokenizer.eos_token_id,
    )

new_tokens = output_ids[0, inputs["input_ids"].shape[-1]:]
answer = tokenizer.decode(new_tokens, skip_special_tokens=True).strip()
print(answer)

Recommended Generation Settings

temperature=0.2 for factual bioinformatics answers
top_p=0.9
repetition_penalty=1.1
max_new_tokens=512

Limitations

The model may produce incorrect or incomplete biological interpretations. Always verify outputs against trusted scientific literature, databases, and domain experts.

Citation

bibtex

@misc{gemma4-12b-bioinfo_2026,
  author       = {yashm},
  title        = {gemma4-12b-bioinfo: Fine-Tuned Gemma 4 12B for Bioinformatics},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/yashm/gemma4-12b-bioinfo}}
}